NOVELTY-DRIVEN ONBOARD TARGETING FOR MARS ROVERS

Virtual Conference 19-23 October 2020

Kiri L. Wagstaff1 (kiri.l.wagstaff@jpl.nasa.gov), Raymond Francis1, Hannah Kerner2, Steven Lu1, Favour Nerrise2,James F. Bell III3, Gary Doran1, and Umaa Rebbapragada1

1Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA, 91109, USA2University of Maryland, College Park, MD, 20742, USA

3Arizona State University, Tempe, AZ, 85381, USA

ABSTRACT

Current Mars surface exploration is primarily pre-scripted on a day-by-day basis. Mars rovers have a lim-ited ability to autonomously select targets for follow-upstudy that match pre-defined target signatures. How-ever, when exploring new environments, we are alsointerested in observations that differ from what previ-ously has been seen. In this work, we develop and eval-uate methods for a Mars rover to use novelty to guidethe selection of observation targets with the goal of ac-celerating discovery. In a study comparing three imagecontent representations and five novelty-based rankingmethods, we found that the Isolation Forest identifiedthe largest number of novel targets using a combina-tion of intensity and shape features to represent the can-didate targets. It was followed closely by the LocalRX algorithm using raw pixel features. All algorithmsachieved performance well above alternatives such asrandom selection or selecting the best match to cur-rent science objectives, which do not account for nov-elty.

1 INTRODUCTIONThe Mars Science Laboratory rover (Curiosity) hasbeen exploring the surface of Mars since August 2012.The primary goal of the mission is to determinewhether Mars may have been habitable in the past(or present) [6]. The Mars 2020 rover (Perseverance)launched in July 2020 and will arrive at Mars in Febru-ary 2021. The Perseverance mission will additionallysearch for signs of past microbial life itself and cachepromising samples for a future mission to recover andreturn to the Earth for further study [13].

Mars rover surface operations are planned by a teamof scientists and engineers on the Earth. After review-ing the rover’s latest position, status, and the data thatit collected, the team develops a plan for the rover’snext day that specifies the time and duration of each ac-tivity: drives, data collection, communication passes,etc. The plan is carefully simulated and tested to con-firm that it fits within the available resources (time, en-ergy, viewing opportunities) before it is uplinked to therover.

At the end of each drive, the Mars Science Labora-tory (MSL) rover collects images of its new locationto transmit to Earth as an aid to planning the next day’sinvestigation. In 2016, the rover gained the ability toautonomously select where to point the ChemCam in-strument to acquire compositional spectra for targetsthat match specified science priorities. ChemCam em-ploys a laser-induced breakdown spectrometer to firea laser and then record the resulting plasma spectrumwhich reveals the presence of individual elements [12].ChemCam can reach targets up to seven meters fromthe rover without requiring a physical approach and in-strument contact.

The software onboard the rover that enables decisionsfor autonomous targeting is AEGIS (Autonomous Ex-ploration for Gathering Increased Science) [5]. Itwas originally developed for the Mars ExplorationRovers to detect interesting targets in navigation cam-eras for follow-up observations with the higher resolu-tion, multi-band panoramic camera [3]. For MSL, tar-get detection and ranking is likewise done using navi-gation camera images, and the top-ranking target’s lo-cation is used to command the collection of spectra byChemCam. The science team specifies a target “sig-nature” that expresses the current science priorities interms of target size, shape, smoothness, albedo (aver-age pixel intensity), etc. AEGIS has autonomously se-lected over 250 targets for MSL since 2016 and has ledto increased use of ChemCam by the science team over-all [5].

In this paper, we propose and evaluate a new approachto autonomous targeting that can allow Mars rovers toselect targets in terms of novelty. This capability cancomplement the AEGIS system by identifying addi-tional targets that may not match current science ob-jectives but which present the possibility of a new dis-covery. We compared multiple algorithms for novelty-based ranking on a collection of Mars rover images andassesses how well their selections agreed with an in-dependent manual identification of novel targets. Wefound that the Isolation Forest was most successful atdiscovering novel targets when using features that cap-

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Run novelty

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Figure 1: Novelty-based target ranking identifies targets, extracts features to represent each target, uses novelty detection to rank thetargets in comparison to previously observed targets, and outputs the top choice. Evaluation is conducted by comparing selectionsto manually identified novel targets (gold polygons). Example image was collected by MSL on sol 1683.

ture both intensity and shape attributes. The Local RXalgorithm achieved almost the same level of perfor-mance using the raw pixel features. All five algorithmsperformed well above random selection as well as theuse of AEGIS alone, which was not designed to priori-tize novel targets.

2 NOVELTY-BASED TARGETING FOR MARSROVERS

We propose a new capability for the onboard, au-tonomous selection of follow-up targets by Marsrovers. In addition to the ability to select targets thatmatch pre-defined signatures, we add the option to se-lect targets based on their novelty with respect to whatthe rover has already encountered.

Fig. 1 illustrates the steps in this procedure applied toa navigational camera (Navcam) image taken by theMSL rover on sol (mission day) 1683. Candidate tar-gets are detected using the Rockster [2] image analysissystem, which is part of AEGIS. Rockster segments theimage into individual targets using edge detection andgrouping. It was designed to run efficiently enough, interms of runtime and memory consumption, for opera-tion onboard Mars rovers. The Mars Science Labora-tory rover has a processor that runs at 133 MHz, withonly 16 megabytes of memory available for AEGIS.The result of Rockster is a collection of polygons thatdescribe the outline of each target.

Each target is represented using a standard set of fea-tures, as described in section 2.1. Next, a novelty de-tection method is used to rank the targets in the currentimage in terms of their novelty with respect to the col-lection of targets previously observed on the mission(section 2.2). Finally, the system outputs the top se-lection, which can be used to inform further follow-up

data collection by ChemCam or other instruments onthe rover.

2.1 Target Representation

We investigated three methods for representing the tar-get image content to provide input for the novelty rank-ing algorithms. Each representation converts data fromthe two-dimensional grayscale image that contains thetarget into a one-dimensional feature vector. Targetsvary in size and aspect ratio. The most basic “inten-sity_pixels” representation uses the pixel intensity val-ues directly as features. To provide a consistent dimen-sionality despite different target sizes, we first crop asquare bounding box around each target polygon andresize the cropped image to 64×64 pixels. The croppedimage containing the target is converted into a featurevector with 4096 values.

Second, the “intensity_stats” representation computesseven statistical features to summarize the contents ofthe pixels within the target image. These statisticsinclude the minimum, maximum, mean, and medianpixel values as well as their standard deviation, skew,and kurtosis. Since these aggregate statistics abstractaway from the original pixels, we do not need to resizethe target images to a standard size, so they operate di-rectly on the cropped (bounding box) image. The resultis a 7-dimensional feature vector for each target.

Finally, the “intensity+shape” representation uses ninedomain-specific features computed by the AEGIS sys-tem for its target ranking procedure. This representa-tion makes use of the polygon itself as well as the pixelvalues. Pixel features include the mean and standarddeviation of pixels within the polygon (not the entirebounding box). The geometry of the target is capturedby the area, perimeter, and “ruggedness” of the polygon

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as well as features derived from fitting an ellipse to thepolygon and recording its eccentricity, orientation, andsemi-major and semi-minor axis lengths [4].

2.2 Novelty Ranking Algorithms

We implemented several methods for assigning a nov-elty score to each target, including ranking the tar-gets by reconstruction error when using a lower-dimensional model computed with Principal Compo-nent Analysis (PCA) or DEMUD [11], by sparsity infeature space (Isolation Forest [8]), and by deviationfrom either the global “background” (RX) [10, 1] or alocal window (LRX).

One approach to novelty detection is to compare newobservations to a compact model of content from priorobservations. Any new content that is not recognizedby the model provides a potential indication of nov-elty. We quantify this kind of novelty as the reconstruc-tion error between the new observation and its recon-struction using the model. For example, we use Prin-cipal Component Analysis (PCA) to compute k eigen-vectors of a n × d training data set, project each iteminto the corresponding k-dimensional space, and thenreconstruct the original observation in d-dimensionalspace. The PCA-based novelty score for a feature vec-tor x is:

sPCA(x) = ||x− (UUT (x− µ) + µ)||2 (1)

where U contains the eigenvectors and µ is the meanfeature vector. Since the projection onto U discards in-formation, some details may be lost in the reconstruc-tion, and these increase the reconstruction error score.The DEMUD algorithm [11] uses the same scoringmethod, but it updates U to include each new selectiononce it is made, to reduce the chance of a redundant se-lection. We used a k value of 10 for “intensity_pixels”,7 for “intensity_stats”, and 3 for “intensity+shape” fea-tures.

In contrast, the Isolation Forest [8] identifies items thatare easily separated from the majority of the data set.An Isolation Forest consists of multiple binary trees, inwhich each tree employs a series of randomly chosensplits to partition the feature space. Each split speci-fies a randomly chosen feature and random thresholdvalue. The position of each item in feature space dic-tates its outlier (novelty) score, which is proportional tothe number of splits required to “isolate” the item intoits own cell in feature space. Outliers will tend to have alower score, while inliners will have a higher score. Thefinal isolation forest score for an item x is its averagescore across all trees in the forest. We used the defaultvalue of 100 trees for the Isolation Forest.

The Reed-Xiaoli (RX) detector [10] is an unsupervisedanomaly detection method that has been used success-fully in remote sensing and exploration applications(e.g., [1, 7, 14]). RX computes an anomaly score foreach item x using the Mahalanobis distance betweenthe item and a background distribution:

sRX(x) = (x− µ)T Σ−1(x− µ) (2)

where Σ is the covariance matrix of the “background”.We used a separate training set as the background forRX.

Since we are working with image data, we can also usea variant of RX called Local RX (LRX), which gener-ates a score for each pixel, then averages those scoresto generate a score for the full image. LRX computesµ and Σ using a pair of sliding windows (inner andouter) to define the local “background” around eachpixel as the pixels that fall between the inner and outerwindows. LRX has been widely used to detect smallanomalies in hyperspectral images [9]. In our experi-ments, we used an inner window size of 3 × 3 pixelsand the outer window size was 5× 5 pixels.

3 EXPERIMENTAL RESULTS3.1 Data Set

When AEGIS is used for MSL, it operates on a pre-planned Navigation camera (Navcam) grayscale imagethat is collected by the rover. The Rockster [2] algo-rithm identifies candidate targets within the Navcamimage. The Navcam image ID, Rockster-identified tar-gets, and target prioritization signatures (which expressthe current science priorities and are used to rank can-didate targets) are included as part of the informationsent from the rover to the planning team. AEGIS wasfirst deployed on sol 1343 (May 16, 2016), and ourdata consists of the Navcam images and AEGIS targetsidentified from sol 1343 to 2578 (November 6, 2019).We assembled a data set of 6, 005 targets that had anarea of at least 100 pixels. We accessed the image datadirectly from the Navcam Experiment Data Records(EDRs) from the Planetary Data System (PDS) Imag-ing Node (https://pds-imaging.jpl.nasa.gov/data/msl/MSLNAV_0XXX/). The EDRs are stored as16-bit images, which we converted to 8-bit imagesby multiplying all pixels by a factor of 255/4095,which is the same conversion done onboard MSL forAEGIS.

To create a reference dataset of “novel” targets for eval-uating algorithm ranking performance, co-author R.Francis identified which of the Rockster targets mighthave been considered novel or interesting by the MSLscience team and had not been observed in Navcam im-

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ages on prior sols. For example, Fig. 1 shows two tar-gets identified as novel on sol 1683 because they depictplate-like exposures of eroded layers. Other kinds ofnovel target features include unusual dark-toned mate-rial, light-toned veins, and rough surface textures. Inall we obtained a list of 108 reference novel targets ob-served from sols 1343 to 1703.

For the image shown in Fig. 1, Rockster identified atotal of 11 targets, two of which are novel (gold poly-gons). The DEMUD and PCA algorithms selectedtarget 0 (novel), and the LRX algorithm selected 166(novel). The Isolation Forest selected target 130, andRX selected target 5, neither of which were considerednovel. The target selected by AEGIS (158) was also notconsidered novel, which is unsurprising since AEGIShas a different objective.

3.2 MethodologyWe performed a series of experiments to evaluate theability of each algorithm to identify novel targets.Within our novel target reference data set, we identi-fied 28 sols that had at least one novel reference tar-get. For each of these sols, we ranked all candidatetargets identified by Rockster in the Navcam image us-ing the novelty ranking algorithms described in Sec-tion 2.2. We defined a “prior” (training) data set oftargets for each algorithm that consisted of all targetsobserved (novel or not) on sols prior to the experi-ment sol, e.g., sols 1343 to S-1 where S is the exper-iment sol. This simulates the operational scenario on-board the rover in which the novelty ranking algorithmwould rank new targets based only on data from pre-vious sols as it traverses the Mars surface. We eval-uated each algorithm with the three target represen-tations (“intensity_pixels”, “intensity_stats”, and “in-tensity+shape”) described in Section 2.1. Each rank-ing algorithm provides a novelty score for each target,with targets ranked in order from most novel to leastnovel.

Some algorithms restrict which feature representationsthey can employ. Since LRX operates on local win-dows surrounding each pixel in the target image, itcannot be used with the “intensity_stats” or “inten-sity+shape” features. RX requires that the number ofitems n be greater than the number of features d, be-cause it computes a feature covariance matrix (Equa-tion 2) that is singular if n < d. Since each targethas dimension 64 × 64, the number of features for the“intensity_pixels” representation is 4096. For all solsin our experiments, the number of prior images is lessthan 4096, so we did not evaluate RX with the “inten-sity_pixels” representation.

When AEGIS is used onboard the MSL rover, typi-

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Figure 2: Summary of novelty detection performance usingeach representation. Dotted lines show Random and AEGISalgorithm performance.

cally only one or two targets are chosen by AEGIS forfollow-up observation with ChemCam due to rover re-source constraints. To quantify the performance of thenovelty ranking algorithms in a similar operational sce-nario, we recorded how many novel reference targetswere found in the top two ranked targets for each al-gorithm (“top-2 score”). Although AEGIS was not de-signed as a novelty detection algorithm, we also com-puted its performance on the same task for comparison.As a baseline, we implemented a random ranking al-gorithm that employs a pseudo-random number gener-ator to randomly rank the targets in each experiment.We ran the random algorithm with 10 different randomseeds and reported the average performance.

3.3 Results

The top-2 score for each novelty ranking algorithm,summed across all scenarios, is shown in Fig. 2. In-dividual results for each sol are given in the Appendix.The AEGIS and random algorithms do not employ dif-ferent feature representations, so they each have a sin-gle total top-2 score of 23 (AEGIS) and 14.5 (random,10 trials), respectively. The maximum possible score is54.

The best top-2 score (34) was achieved by the IsolationForest using the “intensity+shape” features (Fig. 2).LRX, DEMUD, and PCA achieved their best scoreswith the raw pixel intensities (33, 32, and 32 respec-tively). The best RX result (32) was achieved withthe “intensity+shape” features. Overall, the best scoreachieved by each algorithm was well above that of ran-dom selection. All novelty algorithms, using their indi-vidual best feature representations, also out-performed

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Figure 3: Agreement between algorithms in terms of the number of common selections within their top two choices

(a) Target 1475-145 (b) Target 1645-45 (c) Target 1631-21

Figure 4: Targets found uniquely by one algorithm (see text)

the AEGIS algorithm. AEGIS was not designed for se-lecting novel targets; its utility in this comparison isto provide insight into how the current onboard sys-tem would perform on the task of novelty detection. Aswe hypothesized, using an algorithm designed to rankitems by their novelty achieves the best result.

4 DISCUSSION4.1 Feature representationsCompared to algorithms that assign a score to each tar-get based on its novelty with respect to targets in theprior data set, LRX scores each pixel in the target imagebased on its novelty with respect to the local pixels sur-rounding it. This makes LRX effective for identifyingsmall features within candidate target images that devi-ate from their surrounding patterns, such as light-tonedveins. These veins are rare within the full target set,but they are often selected by humans for inclusion inthe novel reference target data set. For example, noveltarget 145 from sol 1475 in Fig. 4(a), which contains asmall bright vein, was found only by the LRX methodand no other algorithm when using “intensity_pixels”features.

The Isolation Forest had the lowest performance us-ing raw pixel intensities but the highest score with the

“intensity+shape” features. The Isolation Forest “iso-lates” candidate targets by recursively selecting one ofthe features at random and then partitioning targets byrandomly selecting a split value between the maximumand minimum values of the selected feature. This pro-cess is represented as a tree structure, and the numberof splits (branches) required to isolate a candidate tar-get (averaged over many random trees) is the IsolationForest’s measure of novelty—i.e., fewer splits implies ahigher degree of novelty. Isolating targets in this man-ner using a large number of features as in the case ofraw pixel intensities is likely less effective than whenfewer, more descriptive features such as the “inten-sity+shape” features are used. Examples of two targetsfound only by the Isolation Forest method when usingthe latter features are shown in Fig. 4(b) and Fig. 4(c);both targets contain multiple veins.

4.2 Algorithm agreement

In addition to reporting the agreement between algo-rithm selections and novel reference target selections, itis also useful to assess agreement between algorithmsthemselves. For example, two algorithms that havehigh performance but make different selections couldbe used in complement onboard to promote a diversityof novel target selections. Figs. 3(a)-3(c) show agree-ment between algorithms for the experiments in Section3.3. In each matrix, the entries represent the number ofcommon targets in the selections made by each pair ofalgorithms out of 56 total selections. We omitted thediagonal entries because these represent the agreementof each algorithm with itself. DEMUD and PCA us-ing “intensity_pixels” features (Fig. 3(a)), stand out foragreeing on nearly all selections (55 out of 56). Both al-gorithms rank the images based on reconstruction errorusing k = 10 eigenvectors to model variation within

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Figure 5: Top 2 selections by novelty ranking algorithms onsol 1685 (“intensity+shape” features)

the data set. The reduction from 4096 to 10 featuresyields similar results with the two methods.

For “intensity_stats” (Fig. 3(b)) and “intensity+shape”(Fig. 3(c)) features, we see generally lower agreement(more diversity). RX and iForest are the algorithmswith the highest agreement in these settings, so it seemslikely that distance from “prior” targets (RX) is corre-lated with separability (Isolation Forest) in these repre-sentations. Using “intensity_pixel” features, PCA, DE-MUD, and LRX achieved similarly high top-2 scores(Fig. 2), but LRX had lower agreement with PCA (31)and DEMUD (32), indicating that its selections com-plement theirs. LRX selected 9 targets not chosenby PCA or DEMUD, of which three contained veins,two had rough surface texture, and four showed layer-ing. PCA and DEMUD selected 6 targets not chosenby LRX, of which only one contained a vein. Like-wise, using “intensity+shape” features, PCA, iForest,and RX had similarly high top-2 scores, but PCA madethe same selections as iForest and RX in only 31 to 35cases.

4.3 Novel reference targets

We observed that some targets were selected by mul-tiple novelty ranking algorithms that were not in thenovel reference target list. We investigated thesechoices carefully and found that they revealed criteriathat science team members factor into target prioriti-zation in addition to scientific interest or novelty. Forexample, Fig. 5 shows the top 2 selections made byAEGIS and the novelty ranking algorithms using the“intensity+shape” features. The novel reference tar-gets for this experiment were 17 and 42, chosen be-cause they contain plate-like exposures of eroded lay-ers. While most of the ranking algorithms selected

Figure 6: Top 2 selections by novelty ranking algorithms onsol 1400 (“intensity_stats” features)

these targets, some also selected target 3. While tar-get 3 contains the same plate-like exposures as targets17 and 42, target 3 is farther away from the rover andis partially covered by sand. While target 3 is a validchoice from a novelty perspective, this target is not asdesirable as 17 and 42 because the follow-up Chem-Cam measurement of target 3 would likely hit the sandrather than the rock itself, and a measurement of therock composition is generally more desirable to the sci-ence team.

Fig. 6 shows another experiment from sol 1400 using“intensity_stats” features. In this experiment, target231 was the novel reference target, desirable because itis bedrock that is notably light-toned. DEMUD, PCA,and AEGIS did select target 231, but the algorithmsalso prioritized other targets (51, 156, 227, and 127).These other targets, originally identified as candidatetargets by the Rockster algorithm, appear to merge mul-tiple rocks into a single target and as a result might haveuncommon feature values that cause them to be priori-tized by the novelty ranking algorithms.

5 CONCLUSIONS AND FUTURE WORKWe propose a new capability for onboard data analysisfor Mars rovers in which they can assess the novelty ofeach candidate target and use this information to informautonomous decisions about which targets merit addi-tional follow-up study. Novelty-based targeting wouldnot replace the method currently used by AEGIS on-board MSL to identify targets that best match currentscience objectives. Instead, mission scientists and plan-ners could selectively employ novelty-based targetingto complement the AEGIS selections (e.g., one science-based and one novelty-based target per drive). In addi-tion, in cases where resources allow for only a single

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Table 1: Number of novel reference targets found in each al-gorithm’s top two selections, with “intensity_pixels” features

sol DEMUD iForest LRX PCA1347 1 1 1 11371 1 1 0 11400 0 0 0 01439 2 1 1 21469 0 0 0 01475 1 0 2 11519 0 0 0 01521 2 1 2 21583 0 0 0 01605 2 1 2 21612 0 1 1 01629 2 2 2 21631 1 1 1 11636 2 2 1 21645 2 2 2 21660 2 2 2 21666 1 1 1 11672 2 2 2 21673 2 0 0 21676 0 0 1 01683 1 0 2 11684 1 0 1 11685 2 2 2 21686 2 1 2 21690 1 1 2 11697 0 1 1 01699 1 2 1 11703 1 2 1 1Total (of 54) 32 27 33 32

follow-up target to be chosen, planners could specifya threshold on the novelty score, rather than a simpleranking, to determine when the rover has encountereda target of sufficient novelty to supersede the target thatbest matches current science priorities.

An important consideration for any use of novelty-based ranking onboard a Mars rover is the resources(memory and computation) that it requires. Our nextstep will be to assess each algorithm in terms of runtimeand memory consumption to determine how it wouldoperate given only 16 MB of RAM and a RAD750 pro-cessor (133 MHz). In addition, we will evaluate thesemethods to rank targets identified in color images suchas those collected by the Mastcam instrument on MSL.Previous work has shown that spectral information isleveraged in different ways by these algorithms [7], andthe best algorithm choice may be different when colorinformation is available.

Appendix A: Per-sol Experiment Results

Performance for each novelty ranking algorithm foreach sol scenario we evaluated is given in Tab. 1 (“in-tensity_pixels”), Tab. 2 (“intensity_stats”), and Tab. 3(“intensity+shape”). The bottom row of each table

Table 2: Number of novel reference targets found in eachalgorithm’s top two selections, with “intensity_stats” features

sol DEMUD iForest PCA RX1347 1 1 1 11371 1 1 1 01400 0 1 1 01439 1 2 1 11469 1 0 0 01475 1 1 2 11519 0 0 0 01521 2 1 2 11583 1 1 1 11605 2 2 2 21612 1 1 0 01629 2 2 2 21631 2 2 2 21636 2 1 1 11645 0 0 0 01660 2 1 2 11666 0 0 0 01672 2 2 2 21673 0 0 0 01676 0 0 0 01683 1 0 0 11684 1 0 1 01685 1 1 1 11686 1 1 2 21690 0 0 0 01697 1 1 0 01699 2 1 1 11703 1 0 0 1Total (of 54) 29 23 25 21

gives the total score across all sols. The AEGIS andrandom algorithms do not employ different feature rep-resentations, so they each have a single total top-2 scoreof 23 (AEGIS) and 14.5 (random, 10 trials), respec-tively. The maximum possible score is 54.

AcknowledgmentsWe thank the NASA Center Innovation Fund for sup-porting this work. Part of this research was carried outat the Jet Propulsion Laboratory, California Institute ofTechnology, under a contract with the National Aero-nautics and Space Administration. Copyright 2020. Allrights reserved.

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Table 3: Number of novel reference targets found in each al-gorithm’s top two selections, with “intensity+shape” features

sol DEMUD iForest PCA RX1347 1 1 1 01371 0 0 0 01400 0 0 0 01439 1 1 2 01469 1 1 1 11475 0 2 1 11519 0 0 0 01521 2 2 1 21583 1 1 1 11605 1 1 1 11612 1 1 1 11629 1 2 2 21631 1 1 1 21636 1 1 2 21645 2 2 2 21660 1 1 1 11666 2 2 2 21672 2 2 2 21673 0 2 0 11676 0 1 1 11683 1 2 2 21684 1 1 0 11685 1 2 1 21686 2 2 2 21690 1 1 1 11697 0 0 0 01699 2 1 2 11703 0 1 1 1Total (of 54) 26 34 31 32

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